Landslide disasters frequently occur along the highway G30 in the Guozigou Valley, the corridor of energy, material, economic and cultural exchange, etc., between Yili and other cities of China and Central Asia. However, little attention has been paid to assess the detailed landslide susceptibility of the strategically important highway, especially with high spatial resolution data and the generative presence-only MaxEnt model. Landslide susceptibility assessment (LSA) is a first and vital step for preventing and mitigating landslide hazards. The goal of the current study was to perform LSA for the landslide-prone highway G30 in Guozigou Valley, China with the aid of GIS tools and Chinese high resolution Gaofen-1 (GF-1) satellite data, and analyze and compare the performance of the maximum entropy (MaxEnt) model and logistic regression (LR). Thirty five landslides were determined in the study region, using GF-1 satellite data, official data, and field surveys. Seven landslide conditioning factors, including altitude, slope, aspect, gully density, lithology, faults density, and NDVI, were used to investigate their existing spatial relationships with landslide occurrences. The LR and MaxEnt model performance were assessed by the receiver operating characteristic curve, presenting areas under the curve equal to 0.85 and 0.94, respectively. The performance of the MaxEnt model was slightly better than that of the LR model. A landslide susceptibility map was created through reclassifying the landslides occurrence probability with the classification method of natural break. According to the MaxEnt model results, 3.29% and 3.82% of the study region is highly and very highly susceptible to future landslide events, respectively, with the highest landslide susceptibility along the highway. The generated landslide susceptibility map could help government agencies and decision-makers to make wise decisions for preventing or mitigating landslide hazards along the highway and design schemes of highway engineering and maintenance in Guozigou Valley, the mountainous areas.

The Ili River Delta (IRD) is the largest delta in the arid zone of Central Asia. Since the 1970s, the entire delta system has undergone a series of changes due to climate change and the impoundment of the Kapchagay Reservoir upstream of the delta, triggering an ecological crisis. Wetlands play a crucial ecological role in biodiversity conservation. Most studies have mainly focused on the response of vegetation and soil microbial to ecological changes in the delta, ignoring the dynamic processes of wetlands changes. Hence, such changes in the IRD and the underlying mechanisms need to be investigated in depth. In this study, wetlands in the IRD from 1975 to 2020 were extracted based on Landsat images using the object-oriented method; changes in the wetland area, wetland landscape pattern, NDVI, and NPP were analyzed; and the contributions of natural and human factors to wetland evolution were quantified. The results indicated the following: (1) From 1975 to 2020, the wetland area of the IRD showed an increasing trend, and changes in the wetland area were mainly found in the middle part of the delta near the Saryesik Peninsula. (2) The wetland landscape pattern in the IRD changed markedly from 1975 to 2020. The dominant patches of the wetland in the middle of the delta continued to expand; the patch aggregation index (AI) increased, and the landscape fragmentation index (LFI) decreased. (3) From 2000 to 2020, the average annual normalized difference vegetation index (NDVI) and net primary productivity (NPP) in the IRD increased, which is consistent with the change in wetland expansion. (4) Inflow to the delta from the Ili River and the water level of Balkhash Lake are significantly correlated with the wetland area, which are the dominant factors driving wetland evolution; and water evaporation from the Kapchagay Reservoir and irrigation water diversion on the left bank of the reservoir obviously intensified the process of lake water level decline and wetland degradation during 1970 to 1985. These results can provide scientific background for making informed ecological protection decisions in the IRD under the impacts of climate change and human activities.